Claude 24f816b6a3
Consolidate 22 sibling repos into layered organism structure
Place useful parts of the surrounding repos into sica-fondt by layer, per the
body model (Ada = membrane; brain/endocrine/capabilities/knowledge non-Ada):

- brain/        LLM reasoning + providers (dapr, hermes, MoMoA)
- capabilities/ REPRAG sidecars: hermes tools/skills, dapr tools, parallel
                dispatch, A51 channels, and the OSINT cluster
- knowledge/    LORAG corpus: 754 cyber-skills, agency personas, secure-coding,
                MITRE ATT&CK data
- reference/    defensive threat-reference (C3, shhbruh doc) + AdaYaml parser

License handling: AGPL sources (worldosint, advanced_evolution, mercury,
Reticulum) and GPL DeTTECT are SPEC-only clean-room/port descriptions — no
copyleft code copied. MIT/Apache/data parts copied as working trees.

Safety: shhbruh escape/persistence material and C3 covert-C2 kept as reference
only, not wired into the running organism. See CONSOLIDATION.md.

https://claude.ai/code/session_01UehUqEXXJJCsHoA4voCU5c
2026-06-10 06:53:01 +00:00

30 lines
581 B
Elixir

alias GradingClient.Answer
to_answers = fn module_name, answers ->
Enum.map(answers, fn data ->
%Answer{
module_id: module_name,
question_id: data.question_id,
answer: data.answer,
help_text: data[:help_text]
}
end)
end
owasp_questions = [
%{
question_id: 1,
answer: :entry_granted_op2,
help_text: "Research MD5 Rainbow Tables"
},
%{
question_id: 2,
answer: :plug,
help_text: "Check the changelog for the next minor or major release of each option."
}
]
List.flatten([
to_answers.(OWASP, owasp_questions)
])